A typical populated geographical region such as a city may be divided into numerous quarters. Different quarters of the geographical region may have people of different socio-economic backgrounds living therein, and the basic assumption is that neighborhoods in any given geographical region generally exhibits similar human activity patterns, such as similar lifestyles and spending habits. Over the recent past, demographic clustering of such geographical region have been explored in various application dimensions such as urban planning, market research, targeted advertising and setting up of commercial and welfare establishments.
Prior art literature discloses various demographic clustering techniques. A majority of the existing solutions relies on supervised manual efforts, such as door to door surveys, and census data; such as National Readership Survey (NRS) which classifies living population of a particular geographical region based on social grades, age, income and similar information pertaining to such population. Prior art literature also discloses applied geo-demographic segmentation systems, like ACORN, MOSAIC, PSYTE, and Tapestry Segmentation, which are employed to segment the living population based on census data, consumer household and individual data collated from a number of governmental and commercial sources. While using any such conventional techniques, extraction of demographic information is largely dependent on household survey records, transaction data, geo-demographic data, and lifestyle data of population residing in said geographical region. Such manual demographic clustering solution(s) exhibit practical constraints of long turn-around time, investment of significant manpower and money.
While on the other hand, satellite imagery of such a geographical region is routinely employed in various environmental applications pertaining to said geographical region such as monitoring forest coverage, water bodies, urban growth etc. For example, satellite imagery has also been used to identify a particular area as containing slums or no slums. However, the scope of said prior art is confined to distinguishing between slums or no slums. It does not discover other geo-demographic clusters. Satellite imagery has also been used to discover individual structures such as buildings, tents etc. however, said prior art does not focus on finding geographical conglomeration of structures or characteristics of neighborhoods.
Other forms of aggregate neighborhood data, such as multimodal sensory data pertaining to telecommunication, traffic-flow data, postal, social media, weather, and air quality have also been used for geo-demographic classification. Such data signify human activity pattern and has enormous potential of complementing static view of satellite imagery of given geographical region. However, the prior art literature has never explored application of satellite imagery in conjugation with multimodal sensory data in geo-demographic analysis or clustering.
The prior art literature has illustrated application of satellite imagery and multimodal sensory data pertaining to human activity for geo-demographic clustering separately and in a different way. However, use of unsupervised learning methods for analyzing big multimedia and multimodal data including satellite imagery and multimodal sensory data in fusion for geo-demographic clustering is still considered as one of the biggest challenges of the technical domain.